MIT: Identifying High-Res Images From Low-Res Images

If you're like most Internet users, you are totally dependent on search engines. They have become our obedient servants, separating the wheat from the chaff. Current search engine technology works really well with text, but not so well with pictures.

MIT researchers are working hard to change that. Among the first steps is seeing how much humans need to know before they can identify an image. The simple answer is, not much. If you're surprised by that, you're not alone.

Antonio Torralba, an assistant professor in MIT's Computer Science and Artificial Intelligence Laboratory, took images, reduced their resolution, and then measured the results. "We are able to recognize what is in images, even if the resolution is very low, because we know so much about images," he says. "The amount of information you need to identify most images is about 32 by 32."

That's 1,024 pixels, or about 10% of the data in a standard 100-by-100-pixel Google Images thumbnail. Cutting the density of the data makes the job of the analysis software that much simpler, and Professor Torralba thinks the size can be cut further, maybe down to 256 pixels per image. These less-dense proxies will speed browsing through huge image databases.

A first step in making this practical will be matching known images, those identified with captions for instance, with unknown images.

"We're trying to find very short codes for images," says Torralba, "so that if two images have a similar sequence [of numbers], they are probably similar--composed of roughly the same object, in roughly the same configuration."

There is a lot of upside potential for this technology, but also a more sinister worry: Will the ability to use low-res imagery, like a surveillance camera might provide, allow businesses and governments to track us even more easily as we live our lives? Probably.

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